Keywords: AI Diffusion Models, Multimodal
Motivation: The medical imaging diagnosis of Alzheimer's disease (AD) often relies on multi-modal images, yet obtaining these can be challenging due to high costs and short-lived tracers.
Goal(s): This research aims to address this limitation by developing a generative model that utilizes MRI to enhance the transformation from 18F-FDG PET to 18F-AV-45 PET images.
Approach: Using MRI as an enhancement modality, our approach enables faster acquisition of images to aid clinical diagnosis.
Results: The experimental results demonstrate the effectiveness and superiority of our method, offering a viable solution for more accessible and efficient AD diagnosis.
Impact: This method significantly enhances diagnostic efficiency in Alzheimer's disease by enabling quick, cost-effective image generation, reducing reliance on expensive, short-lived tracers, and providing accessible support for clinicians, ultimately advancing multi-modal medical imaging practices in neurodegenerative diseases.
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